（一）前情

这个工作已经有大佬用在自己的工程里了，他的帖子链接：https://blog.csdn.net/weixin_45829462/article/details/120372921
但他的这个lite主要不是研究repvgg的，是做移动端的，但是里面加了这个repvgg
他的代码链接：https://github.com/ppogg/YOLOv5-Lite/tree/ca7ed7ca0bb578fe6e5eaa777e84f661ad457e49
我是看了看他的代码，然后把关于repvgg的地方加到了自己的yolov5-7.0中（但后续我没用seg去做训练，就正常训练）
后续我还试着把rep-vgg官方的预训练模型的backbone移植到了自己的yolov5-repvgg训练了50epoch的pt中，效果不好。

理论知识：
我先看了个原作者写的帖子：结构重参数化：利用参数转换解耦训练和推理结构，大致看了看
后来去看了原作者的一个视频：丁霄汉：结构重参数化是怎么来的【深度学习】【直播回放】
主要是讲作者写这个的一个思路，和为什么写这个，有助于理解
又看了个我比较喜欢的up主的讲解：RepVGG网络讲解
还有他的csdn：RepVGG网络简介

（二）YOLOv5改进之结合​RepVGG

1.配置common.py文件

# build repvgg block
# -----------------------------
def conv_bn(in_channels, out_channels, kernel_size, stride, padding, groups=1):
    result = nn.Sequential()
    result.add_module('conv', nn.Conv2d(in_channels=in_channels, out_channels=out_channels,
                                        kernel_size=kernel_size, stride=stride, padding=padding, groups=groups,
                                        bias=False))
    result.add_module('bn', nn.BatchNorm2d(num_features=out_channels))
    return result
#RepVGGBlock
class RepVGGBlock(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=3,
                 stride=1, padding=1, dilation=1, groups=1, padding_mode='zeros', deploy=False, use_se=False):
        super(RepVGGBlock, self).__init__()
        self.deploy = deploy
        self.groups = groups
        self.in_channels = in_channels
        padding_11 = padding - kernel_size // 2
        self.nonlinearity = nn.SiLU()
        # self.nonlinearity = nn.ReLU()
        if use_se:
            self.se = SEBlock(out_channels, internal_neurons=out_channels // 16)
        else:
            self.se = nn.Identity()
        if deploy:
            self.rbr_reparam = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
                                         stride=stride,
                                         padding=padding, dilation=dilation, groups=groups, bias=True,
                                         padding_mode=padding_mode)
        else:
            self.rbr_identity = nn.BatchNorm2d(
                num_features=in_channels) if out_channels == in_channels and stride == 1 else None
            self.rbr_dense = conv_bn(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
                                     stride=stride, padding=padding, groups=groups)
            self.rbr_1x1 = conv_bn(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=stride,
                                   padding=padding_11, groups=groups)
            # print('RepVGG Block, identity = ', self.rbr_identity)
    def get_equivalent_kernel_bias(self):
        kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense)
        kernel1x1, bias1x1 = self._fuse_bn_tensor(self.rbr_1x1)
        kernelid, biasid = self._fuse_bn_tensor(self.rbr_identity)
        return kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, bias3x3 + bias1x1 + biasid
    def _pad_1x1_to_3x3_tensor(self, kernel1x1):
        if kernel1x1 is None:
            return 0
        else:
            return torch.nn.functional.pad(kernel1x1, [1, 1, 1, 1])
    def _fuse_bn_tensor(self, branch):
        if branch is None:
            return 0, 0
        if isinstance(branch, nn.Sequential):
            kernel = branch.conv.weight
            running_mean = branch.bn.running_mean
            running_var = branch.bn.running_var
            gamma = branch.bn.weight
            beta = branch.bn.bias
            eps = branch.bn.eps
        else:
            assert isinstance(branch, nn.BatchNorm2d)
            if not hasattr(self, 'id_tensor'):
                input_dim = self.in_channels // self.groups
                kernel_value = np.zeros((self.in_channels, input_dim, 3, 3), dtype=np.float32)
                for i in range(self.in_channels):
                    kernel_value[i, i % input_dim, 1, 1] = 1
                self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device)
            kernel = self.id_tensor
            running_mean = branch.running_mean
            running_var = branch.running_var
            gamma = branch.weight
            beta = branch.bias
            eps = branch.eps
        std = (running_var + eps).sqrt()
        t = (gamma / std).reshape(-1, 1, 1, 1)
        return kernel * t, beta - running_mean * gamma / std
    def forward(self, inputs):
        if hasattr(self, 'rbr_reparam'):
            return self.nonlinearity(self.se(self.rbr_reparam(inputs)))
        if self.rbr_identity is None:
            id_out = 0
        else:
            id_out = self.rbr_identity(inputs)
        return self.nonlinearity(self.se(self.rbr_dense(inputs) + self.rbr_1x1(inputs) + id_out))
    def fusevggforward(self, x):
        return self.nonlinearity(self.rbr_dense(x))

我看网上有些帖子的代码，加到自己工程里有问题，是因为缺conv_bv代码块。自己加上就行了，我这个上面是加进去的。

2.配置yolo.py文件

3.配置yolov5_​​RepVGG.yaml文件

我新建了一个yolov5s-repvgg.yaml

# YOLOv5 馃殌 by Ultralytics, GPL-3.0 license
# Parameters
nc: 4  # number of classes
depth_multiple: 1  # model depth multiple
width_multiple: 1  # layer channel multiple
anchors:
  - [10,13, 16,30, 33,23]  # P3/8
  - [30,61, 62,45, 59,119]  # P4/16
  - [116,90, 156,198, 373,326]  # P5/32
# YOLOv5 v6.0 backbone
backbone:
  # [from, number, module, args]
  [[-1, 1, RepVGGBlock, [64, 3, 2]],  # 0-P1/2
   [-1, 1, RepVGGBlock, [64, 3, 2]], # 1-P2/4
   [-1, 1, RepVGGBlock, [64, 3, 1]], # 2-P2/4
   [-1, 1, RepVGGBlock, [128, 3, 2]], # 3-P3/8
   [-1, 3, RepVGGBlock, [128, 3, 1]],
   [-1, 1, RepVGGBlock, [256, 3, 2]], # 5-P4/16
   [-1, 13, RepVGGBlock, [256, 3, 1]],
   [-1, 1, RepVGGBlock, [512, 3, 2]], # 7-P4/16
  ]
# YOLOv5 head
head:
  [[-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
   [-1, 1, C3, [256, False]],  # 11
   [-1, 1, Conv, [128, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
   [-1, 1, C3, [128, False]],  # 15 (P3/8-small)
   [-1, 1, Conv, [128, 3, 2]],
   [[-1, 12], 1, Concat, [1]],  # cat head P4
   [-1, 1, C3, [256, False]],  # 18 (P4/16-medium)
   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 8], 1, Concat, [1]],  # cat head P5
   [-1, 1, C3, [512, False]],  # 21 (P5/32-large)
   [[15, 18, 21], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]

至于number那里为什么是1，1，1，1，3，1，13，1
depth_multiple: 1 # model depth multiple
width_multiple: 1 # layer channel multiple

是为了和repvgg原网络的A1结构保持一致，方便后续做一个预训练模型的迁移

训练结果

开始训练

python train.py --weights ./weights/yolov5s.pt --data ./data/coco-repvgg.yaml --epochs 50 --img 640 --cfg models/yolov5s-repvgg.yaml --name yolov5s-repvgg

这里加不加预训练都行，但是我的实验结果发现，加了预训练模型，会导致训练50epoch，没有原来的yolov5s训练50epoch以后的结果好。

但是yolov5s与改进的yolov5-repvgg俩模型，都不加预训练模型，一起训50，yolov5-repvgg的效果是要更好的！也就是很有效其实。

后面迁移repvgg预训练模型的代码，后续也会发出来。